object KolmogorovSmirnovTest
Conduct the twosided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution. For more information on KS Test:
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 @Since( "2.4.0" )
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 KolmogorovSmirnovTest.scala
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 KolmogorovSmirnovTest
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def
test(dataset: Dataset[_], sampleCol: String, distName: String, params: Double*): DataFrame
Convenience function to conduct a onesample, twosided KolmogorovSmirnov test for probability distribution equality.
Convenience function to conduct a onesample, twosided KolmogorovSmirnov test for probability distribution equality. Currently supports the normal distribution, taking as parameters the mean and standard deviation.
 dataset
A
Dataset
or aDataFrame
containing the sample of data to test sampleCol
Name of sample column in dataset, of any numerical type
 distName
a
String
name for a theoretical distribution, currently only support "norm". params
Double*
specifying the parameters to be used for the theoretical distribution. For "norm" distribution, the parameters includes mean and variance. returns
DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:
pValue: Double
statistic: Double
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 @Since( "2.4.0" ) @varargs()

def
test(dataset: Dataset[_], sampleCol: String, cdf: Function[Double, Double]): DataFrame
Javafriendly version of
test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)
Javafriendly version of
test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)
 Annotations
 @Since( "2.4.0" )

def
test(dataset: Dataset[_], sampleCol: String, cdf: (Double) ⇒ Double): DataFrame
Conduct the twosided KolmogorovSmirnov (KS) test for data sampled from a continuous distribution.
Conduct the twosided KolmogorovSmirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution.
 dataset
A
Dataset
or aDataFrame
containing the sample of data to test sampleCol
Name of sample column in dataset, of any numerical type
 cdf
a
Double => Double
function to calculate the theoretical CDF at a given value returns
DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:
pValue: Double
statistic: Double
 Annotations
 @Since( "2.4.0" )

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